Grant utilization based other cell interference estimation

ABSTRACT

Mobile broadband traffic has been exploding in wireless networks resulting in an increase of interferences and reduced operator control. Networks are also becoming more heterogeneous putting additional demand in interference management. Scheduler schedules uplink transmissions from UEs based on a load prediction algorithm that typically assumes worst case. However, UEs do not always use full power granted, and thus, much of granted radio resources are wasted. To address these and other issues, technique(s) to accurately predict/estimate other cell interferences and thermal noise separately and to accurately predict/estimate grant utilization probability and variance is(are) described. Inventive estimation technique(s) can be used to schedule UEs to more fully utilize available radio resources. Extended Kalman filtering can be adapted for use in estimation providing low order computational complexity.

RELATED APPLICATION

This application may be related, at least in part, to U.S. applicationSer. No. 13/488,187 entitled “OTHER CELL INTERFERENCE ESTIMATION” filedJun. 4, 2012; and to U.S. application Ser. No. 13/656,581 entitled“METHOD, APPARATUS, AND SYSTEM FOR INTERFERENCE AND NOISE ESTIMATION”filed Oct. 12, 2012, the entire contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The technical field of the present disclosure generally relates toestimating other cell interferences in a wireless network. Inparticular, the technical field relates to apparatus(es), method(s),and/or system(s) for estimating other cell interferences using grantutilizations.

BACKGROUND

Recently, at least the following trends have emerged in field ofcellular telephony. First, mobile broadband traffic has been explodingin wireless networks such as WCDMA (wideband code division multipleaccess). The technical consequence is a corresponding steep increase ofthe interference in these networks, or equivalently, a steep increase ofthe load. This makes it important to exploit the load headroom that isleft in the most efficient way.

Second, cellular networks are becoming more heterogeneous, with macroRBSs (radio base station) being supported by micro and pico RBSs attraffic hot spots. Furthermore, home base stations (e.g., femto RBSs)are emerging in many networks. This trend puts increasing demands oninter-cell interference management.

Third, the consequence of the above is a large increase of the number ofnetwork nodes in cellular networks, together with a reduced operatorcontrol. There is therefore a strong desire to introduce moreself-organizing network (SON) functionality. Such functionality maysupport interference management by automatic interference thresholdsetting and adaptation, for a subset of the nodes of the cellularnetwork.

As a result, there are problems that can hinder providing efficientservice. In WCDMA for example, the UEs (user equipments) may or may notutilize the power granted by the EUL (enhanced uplink) scheduler. Thisleads to an inaccuracy of the load prediction step, where the schedulerbases its scheduling decision on a prediction of the resulting airinterface load of the traffic it schedules. This is so since the 3GPPstandard has an inherent delay of about 5 TTIs (transmission timeinterval) from the scheduling decision until the interference powerappears over the air interface. Also the WCDMA load prediction does notaccount for all imperfections in the modeling of an UL (uplink) radioreceiver. This can lead to additional inaccuracies in the loadprediction and estimation steps.

The inventor is not aware of readily available any practical cellinterference estimation algorithm that can provide other cellinterference estimates with an inaccuracy better than 10-20%, and doesso with close to transmission time interval (TTI, typically 2 ms or 10ms) bandwidth (typically 250 or 50 Hz) over the power and load ranges ofinterest. As a result, it is not possible to make optimal schedulingdecisions since the exact origin of the interference power in the UL isunknown.

Load Estimation without Other Cell Interference Estimation

Following is a discussion on measurement and estimation techniques tomeasure instantaneous total load on the uplink air interface given in acell of a WCDMA system. In general, a load at the antenna connector isgiven by noise rise, also referred to as rise over thermal, RoT(t),defined by:

$\begin{matrix}{{{{RoT}(t)} = \frac{P_{RTWP}(t)}{P_{N}(t)}},} & (1)\end{matrix}$where P_(N)(t) is the thermal noise level as measured at the antennaconnector. For the purposes of discussion, P_(RTWP)(t) may be viewed asthe total wideband power defined by:

$\begin{matrix}{{{P_{RTWP}(t)} = {{\sum\limits_{i = 1}^{I}\;{P_{i}(t)}} + {P_{other}(t)} + {P_{N}(t)}}},} & (2)\end{matrix}$also measured at the antenna connector. The total wideband powerP_(RTWP)(t), is unaffected by any de-spreading applied. In (2),P_(other)(t) represents the power as received from one or more cells ofthe WCDMA system other than an own cell. The P_(i)(t) are the powers ofthe individual users. One major difficulty of any RoT estimationtechnique is in the inherent inability to separate the thermal noiseP_(N)(t) from the interference P_(other)(t) from other cells.

Another specific problem that needs to be addressed is that the signalreference points are, by definition, at the antenna connectors. Themeasurements are however obtained after the analog signal conditioningchain, in the digital receiver. The analog signal conditioning chainintroduces a scale factor error of about 1 dB (1-sigma) that isdifficult to compensate for. Fortunately, all powers of (2) are equallyaffected by the scale factor error so when (1) is calculated, the scalefactor error is cancelled as follows:

$\begin{matrix}{{{RoT}^{{Digital}\mspace{14mu}{Receiver}}(t)} = {\frac{P_{RTWP}^{{Digital}\mspace{14mu}{Receiver}}(t)}{P_{N}^{{Digital}\mspace{14mu}{Receiver}}(t)} = {\frac{{\gamma(t)}{P_{RTWP}^{Antenna}(t)}}{{\gamma(t)}{P_{N}^{Antenna}(t)}} = {{{RoT}^{Antenna}(t)}.}}}} & (3)\end{matrix}$

To understand the fundamental problem of interferences from other cellswhen performing load estimation, note that:P _(other)(t)+P _(N)(t)=E[P _(other)(t)]+E[P _(N)(t)]+ΔP _(other)(t)+ΔP_(N)(t).  (4)where E[ ] denotes a mathematical expectation and where Δ denotes avariation around the mean. The fundamental problem can now be clearlyseen. Since there are no measurements available in the RBS that arerelated to the other cell interference, a linear filtering operation canat best estimate the sum E[P_(other)(t)]+E[P_(N)(t)]. This estimatecannot be used to deduce the value of E[P_(N)(t)]. The situation is thesame as when only the sum of two numbers is available. Then there is noway to figure out the individual values of E[P_(other)(t)] andE[P_(N)(t)]. It has also been formally proved that the thermal noisepower floor is not mathematically observable in case there is a non-zeromean other cell interference present in the uplink (UL).

FIG. 1 illustrates a conventional algorithm that estimates a noisefloor. The illustrated algorithm is referred to as a sliding windowalgorithm, and estimates the RoT as given by equation (1). The mainproblem solved by this conventional estimation algorithm is that it canprovide an accurate estimation of the thermal noise floor N(t). Since itis not possible to obtain exact estimates of this quantity due to theother cell interference, the estimator therefore applies anapproximation, by consideration of a soft minimum as computed over arelative long window in time. It is important to understand that thisestimation relies on the fact that the noise floor is constant over verylong periods of time (disregarding the small temperature drift).

One significant disadvantage of the sliding window algorithm is that thealgorithm requires a large amount of storage memory. This becomesparticularly troublesome in case a large number of instances of thealgorithm is needed, as may be the case when base stations serve manycells and when techniques like 4-way receiver diversity is introduced inthe WCDMA UL. A recursive algorithm has been introduced to reduce thememory consumption. Relative to the sliding window algorithm, therecursive algorithm can reduce the memory requirement by a factor ofmore than one hundred.

Load Prediction without Interference Power Estimation

Following is a discussion on techniques to predict instantaneous load onthe uplink air interface ahead in time. The scheduler uses thisfunctionality. The scheduler tests different combinations of grants todetermine the best combinations, e.g., maximizing the throughput. Thisscheduling decision will only affects the air interface load after anumber of TTIs (each such TTI a predetermined time duration such as 2 or10 ms), due to grant transmission latency and UE latency before the newgrant takes effect over the air interface.

In a conventional SIR (signal-to-interference ratio) based method, theprediction of uplink load, for a tentative scheduled set of UEs andgrants, is based on the power relation defined by:

$\begin{matrix}{{{{P_{RTWP}(t)} - {P_{N}(t)}} = {{\sum\limits_{i = 1}^{N}\;{{L_{i}(t)}{P_{RTWP}(t)}}} + {P_{other}(t)}}},} & (5)\end{matrix}$where L_(i)(t) is the load factor of the i-th UE of the own cell. Asindicated, P_(other)(t) denotes the other cell interference. The loadfactors of the own cell are computed as follows. First, note that:

$\begin{matrix}{{\left. \begin{matrix}{{\left( {C/I} \right)_{i}(t)} = \frac{P_{i}(t)}{{P_{RTWP}(t)} - {\left( {1 - \alpha} \right)P_{i}}}} \\{= \frac{{L_{i}(t)}{P_{RTWP}(t)}}{{P_{RTWP}(t)} - {\left( {1 - \alpha} \right){L_{i}(t)}{P_{RTWP}(t)}}}} \\{= \frac{L_{i}(t)}{1 - {\left( {1 - \alpha} \right){L_{i}(t)}}}}\end{matrix}\Leftrightarrow{L_{i}(t)} \right. = \frac{\left( {C/I} \right)_{i}(t)}{1 + {\left( {1 - \alpha} \right)\left( {C/I} \right)_{i}(t)}}},\mspace{31mu}{i = 1},\ldots\mspace{14mu},I,} & (6)\end{matrix}$where I is the number of UEs in the own cell and α is theself-interference factor. The carrier to interference values,(C/I)_(i)(t), i=1, . . . , I, are then related to the SINR (measured onthe DPCCH channel) as follows:

$\begin{matrix}{{{\left( {C/I} \right)_{i}(t)} = {\frac{{SINR}_{i}(t)}{W_{i}}\frac{RxLoss}{G} \times \left( {1 + \frac{\begin{matrix}{{\beta_{{DPDCH},i}^{2}(t)} + {\beta_{{EDPCCH},i}^{2}(t)} +} \\{{{n_{{codes},i}(t)}{\beta_{{EDPDCH},i}^{2}(t)}} + {\beta_{{HSDPCCH},i}^{2}(t)}}\end{matrix}}{\beta_{DPCCH}^{2}(t)}} \right)}},\mspace{79mu}{i = 1},\ldots\mspace{14mu},{I.}} & (7)\end{matrix}$

In (7), W_(i) represents the spreading factor, RxLoss represents themissed receiver energy, G represents the diversity gain and the β:srepresent the beta factors of the respective channels. Here, inactivedata channels are assumed to have zero data beta factors.

The UL load prediction then computes the uplink load of the own cell bya calculation of (6) and (7) for each UE of the own cell, followed by asummation:

$\begin{matrix}{{{L_{own}(t)} = {\sum\limits_{i = 1}^{I}{L_{i}(t)}}},} & (8)\end{matrix}$which transforms (5) to:P _(RTWP)(t)=L _(own)(t)P _(RTWP)(t)+P _(other)(t)+P _(N)(t).  (9)Dividing (9) by P_(N)(t) shows that the RoT can be predicted k TTIsahead as:

$\begin{matrix}{{{RoT}\left( {t + {kT}} \right)} = {\frac{{P_{othor}(t)}/{P_{N}(t)}}{1 - {L_{own}(t)}} + {\frac{1}{1 - {L_{own}(t)}}.}}} & (10)\end{matrix}$

In the SIR based load factor calculation, the load factor L_(i)(t) isdefined by (6). However, in a power based load factor calculation, theload factor L_(i)(t) can be defined by:

$\begin{matrix}{{{L_{i}(t)} = \frac{P_{i}(t)}{P_{RTWP}(t)}},{i = 1},\ldots\mspace{14mu},I,} & (11)\end{matrix}$and equations (8)-(10) may be calculated based on the load factorL_(i)(t) of (11) to predict the RoT k TTIs ahead. An advantage of thepower based load factor calculation is that the parameter dependency isreduced. But on the downside, a measurement of the UE power is needed.Older HW may continue to rely on (5)-(10).

In heterogeneous networks (HetNets), different kinds of cells are mixed.A problem that arises in Hetnets in that the cells are likely to havedifferent radio properties in terms of (among others):

radio sensitivity;

frequency band;

coverage;

output power;

capacity; and

acceptable load level.

This can be an effect of the use of different RBS sizes (macro, micro,pico, femto), different revisions (different receiver technology, SWquality), different vendors, the purpose of a specific deployment, andso on. An important factor in HetNets is that of the air interface loadmanagement, i.e., the issues associated with the scheduling of radioresources in different cells and the interaction between cells in termsof inter-cell interference.

These issues are exemplified with reference to FIG. 2 which illustratesa low power cell with limited coverage intended to serve a hotspot. Toenable sufficient coverage of the hot spot, an interference suppressingreceiver like the G-rake+ is used. One problem is now that the low powercell is located in the interior of and at the boundary of a specificmacro cell. Also, surrounding macro cells interfere with the low powercell rendering a high level of other cell interference in the low powercell which, despite the advanced receiver, reduces the coverage tolevels that do not allow coverage of the hot spot. As a result, UEs ofthe hot spot are connected to the surrounding macro cells, which canfurther increase the other cell interference experienced by the lowpower cell.

As mentioned, scheduling decisions may be made based on a prediction ofair interface load (e.g., RoT) that will result due to the scheduledgrants to ensure that scheduled load does not exceed the load thresholdsfor coverage and stability. If the load prediction is made assuming theworst case (that all UEs use all granted resources at all times), it canlead to a waste of air-interface resources since the UEs are notrequired to use all granted resources. Thus, it is desirable to estimateand predict the load accurately so as to minimize waste of resources.

SUMMARY

In one or more aspects of the disclosed subject matter, it is proposedthat by accurately determining interferences experienced at an own cell,and in particular, by accurately determining interferences at the owncell due to activities of other cells, the load can be accuratelyestimated and predicted. In addition, one or more of the proposedsolutions enable the determination of the other cell interferences at ornear the bandwidth of the scheduling decisions.

A non-limiting aspect of the disclosed subject matter is directed to amethod performed in a radio network node of a wireless network fordetermining other cell interference applicable at a particular time. Themethod can comprise the step of estimating (710) a grant utilizationprobability p_(grant)(t₁) based at least on a grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₀) and aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at a time t₀to obtain a grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) applicable at a time t₁, in which t₁−t₀=T>0. The methodcan also comprise the step of estimating an interference-and-noise sumP_(other)(t₁)+P_(N)(t₁) based at least on the grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₀) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁. The method can further comprise the step of estimating an other cellinterference P_(other)(t₁) based at least on the interference-and-noisesum estimate {circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁) and a thermal noise estimate {circumflex over (P)}_(N)(t₁)to obtain an other cell interference estimate {circumflex over(P)}_(other)(t₁) applicable at the time t₁.

Another non-limiting aspect of the disclosed subject matter is directedto a computer-readable medium which has programming instructions. When acomputer executes the programming instructions, the computer executesthe method performed in a radio network node of a wireless network asdescribed above for determining other cell interference applicable at aparticular time.

Yet another non-limiting aspect of the disclosed subject matter isdirected to a radio network node of a wireless network. The radionetwork node is structured to determine other cell interferenceapplicable at a particular time. The radio network node can comprise atransceiver structured to transmit and receive wireless signals via oneor more antennas from and to one or more cell terminals located withinthe cell of interest, a communicator structured to communicate withother network nodes, and a scheduler structured to schedule uplinktransmissions from the cell terminals. The scheduler can also bestructured to estimate a grant utilization probability p_(grant)(t₁)based at least on a grant utilization probability estimate {circumflexover (p)}_(grant)(t₀) and an interference-and-noise sum estimate{circumflex over (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀)applicable at a time t₀ to obtain a grant utilization probabilityestimate {circumflex over (p)}_(grant)(t₁) applicable at a time t₁,wherein t₁−t₀=T>0. The scheduler can further be structured to estimatean interference-and-noise sum P_(other)(t₁)+P_(N)(t₁) based at least onthe grant utilization probability estimate {circumflex over(p)}_(grant)(t₀) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁. The scheduler can yet further be structured to estimate an othercell interference P_(other)(t₁) based at least on theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and a thermal noiseestimate {circumflex over (P)}_(N)(t₁) to obtain an other cellinterference estimate {circumflex over (P)}_(other)(t₁) applicable atthe time t₁.

In these aspects, the grant utilization probability p_(grant)(t) canexpress a relationships between radio resource grants scheduled to oneor more cell terminals and radio resource grants used by the same cellterminals applicable at a time t. Each cell terminal can be a wirelessterminal in the cell of interest, and {circumflex over (p)}_(grant)(t)can express an estimate of the grant utilization probabilityp_(grant)(t). The interference-and-noise sum P_(other)(t)+P_(N)(t) canexpress a sum of undesired signals, other than an own cell loadP_(own)(t), applicable at the time t, and {circumflex over(P)}_(other)(t)+{circumflex over (P)}_(N)(t) can express an estimate ofthe interference-and-noise sum estimate P_(other)(t)+P_(N)(t). The owncell load P_(own)(t) can express a sum of signals due to wirelessactivities in the cell of interest. The other cell interferenceP_(other)(t) can express a sum of interferences present in the cell ofinterest due to wireless activities applicable at the time t in one ormore cells other than in the cell of interest, and {circumflex over(P)}_(other)(t) can express an estimate of the other cell interferenceP_(other)(t). A thermal noise P_(N)(t) can express a sum of undesiredsignals present in the cell of interest at the time t other than the owncell load P_(own)(t) and other than the other cell interferenceP_(other)(t), and {circumflex over (P)}_(N)(t₁) can express an estimateof the thermal noise P_(N)(t).

DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosed subject matter will be apparent from the following moreparticular description of preferred embodiments as illustrated in theaccompanying drawings in which reference characters refer to the sameparts throughout the various views. The drawings are not necessarily toscale.

FIG. 1 illustrates a conventional algorithm that estimates a noisefloor;

FIG. 2 illustrates an example scenario of a low power cell with limitedcoverage intended to serve a hotspot;

FIG. 3 illustrates a plot of a grant utilization probability;

FIG. 4 illustrates an example scenario in which other cell interferenceis determined;

FIGS. 5 and 6 respectively illustrate example embodiments of a radionetwork node;

FIG. 7 illustrates a flow chart of example method performed by a radionetwork node to determine an other cell interference;

FIG. 8 illustrates a flow chart of an example process performed by aradio network node to estimate the grant utilization probability and toestimate the interference-and-noise sum;

FIG. 9 illustrates a flow chart of another example process performed bya radio network node to estimate the grant utilization probability andto estimate the interference-and-noise sum;

FIG. 10 illustrates a flow chart of an example process performed by aradio network node to obtain an interference-and-noise sum estimate;

FIG. 11 illustrates a flow chart of an example process performed by aradio network node to determine an other cell interference estimate;

FIG. 12 illustrates a flow chart of yet another example processperformed by a radio network node to estimate the grant utilizationprobability and to estimate the interference-and-noise sum; and

FIG. 13 illustrates a flow chart of an example process performed by aradio network node to perform an (extended) Kalman filter update of apredicted state vector.

DETAILED DESCRIPTION

For purposes of explanation and not limitation, specific details are setforth such as particular architectures, interfaces, techniques, and soon. However, it will be apparent to those skilled in the art that thetechnology described herein may be practiced in other embodiments thatdepart from these specific details. That is, those skilled in the artwill be able to devise various arrangements which, although notexplicitly described or shown herein, embody the principles of thedescribed technology.

In some instances, detailed descriptions of well-known devices,circuits, and methods are omitted so as not to obscure the descriptionwith unnecessary details. All statements herein reciting principles,aspects, embodiments and examples are intended to encompass bothstructural and functional equivalents. Additionally, it is intended thatsuch equivalents include both currently known equivalents as well asequivalents developed in the future, i.e., any elements developed thatperform same function, regardless of structure.

Thus, for example, it will be appreciated that block diagrams herein canrepresent conceptual views of illustrative circuitry embodyingprinciples of the technology. Similarly, it will be appreciated that anyflow charts, state transition diagrams, pseudo code, and the likerepresent various processes which may be substantially represented incomputer readable medium and executed by a computer or processor,whether or not such computer or processor is explicitly shown.

Functions of various elements including functional blocks labeled ordescribed as “processors” or “controllers” may be provided throughdedicated hardware as well as hardware capable of executing associatedsoftware. When provided by a processor, functions may be provided by asingle dedicated processor, by a single shared processor, or by aplurality of individual processors, some of which may be shared ordistributed. Moreover, explicit use of term “processor” or “controller”should not be construed to refer exclusively to hardware capable ofexecuting software, and may include, without limitation, digital signalprocessor (shortened to “DSP”) hardware, read only memory (shortened to“ROM”) for storing software, random access memory (shortened to RAM),and non-volatile storage.

In this document, 3GPP terminologies—e.g., WCDMA, LTE—are used asexamples for explanation purposes. Note that the technology describedherein can be applied to non-3GPP standards, e.g., WiMAX, cdma2000,1xEVDO, etc. Thus, the scope of this disclosure is not limited to theset of 3GPP wireless network systems and can encompass many domains ofwireless network systems. Also, a base station (e.g., RBS, NodeB,eNodeB, eNB, etc.) will be used as an example of a radio network node inwhich the described method can be performed. However, it should be notedthat the disclosed subject matter is applicable to any node, such asrelay stations, that receive wireless signals. Also without loss ofgenerality, mobile terminals (e.g., UE, mobile computer, PDA, etc.) willbe used as examples of wireless terminals that communicate with the basestation.

As indicated above, one major disadvantage of many conventional RoT(t)estimation techniques is in the difficulty in separating the thermalnoise P_(N)(t) from the interference P_(other)(t) from other cells. Thismakes it difficult to estimate the RoT(t), i.e., difficult to estimatethe load as given in equation (1). The other cell interferenceP_(other)(t) in this context may be viewed as a sum of interferencespresent in a cell of interest due to wireless activities applicable attime t in one or more cells other than in the cell of interest. In oneor more aspects, the determination of the other cell interferenceP_(other)(t) involves estimating the other cell interference. For thepurposes of this disclosure, estimations of parameters are indicatedwith a “^” (caret) character. For example, {circumflex over(P)}_(other)(t) may be read as an estimate of the other cellinterference P_(other)(t).

There are known techniques to determine the other cell interferenceestimate {circumflex over (P)}_(other)(t). These conventional techniquesassume that the powers of all radio links are measured in the uplinkreceiver. This assumption is not true in many instances today. The powermeasurement is associated with difficulties since:

-   -   In WCDMA for example, the uplink transmission is not necessarily        orthogonal, which can cause errors when the powers are        estimated;    -   The individual code powers are often small, making the SNRs        (signal-to noise ratio) low as well. This further contributes to        the inaccuracy of the power estimates.

One major problem associated with the conventional other cellinterference estimation techniques is that the sum of other cellinterference and thermal noise P_(other)(t)+P_(N)(t)(referred to as theinterference-and-noise sum) needs to be estimated through high orderKalman filtering. The primary reason is that all powers of the UEs needto be separately filtered using at least one Kalman filter state per UEwhen such techniques are used. This step therefore is associated with avery high computational complexity. There are techniques that can reducethis computational complexity, but the complexity can be still too highwhen the number of UEs increases. In these conventional solutions, thethermal noise floor N(t) is estimated as described above, i.e.,{circumflex over (N)}(t) is determined followed by a subtraction toarrive at an estimate of the other cell interference {circumflex over(P)}_(other)(t).

In the existing solutions, the EUL utilizes a scheduler that aims tofill the load headroom of the air interface, so that the different UErequests for bitrates are met. As stated above, the air-interface loadin WCDMA is determined in terms of the noise rise over the thermal powerlevel, i.e., the RoT(t), which is estimated at the base station.

When evaluating scheduling decisions, the scheduler predicts the loadthat results form the scheduled grants, to make sure that the scheduledload does not exceed the load thresholds for coverage and stability.This can be complicated since the grant given to a UE only expresses alimit on the UL power the UE is allowed to use. However, the UE mayactually use only a portion of its grant. The conventional scheduleroften has to make a worst case analysis, assuming that all UEs will usetheir grants at all times. But in reality, UEs in general have arelatively low utilization of grants. This is evident from fieldmeasurements as those depicted in FIG. 3. The plot indicates a grantutilization of only about 25%. In other words, a significant amount(about 75%) of air-interface resources is wasted.

To summarize, there is a lack of technology that can estimate the othercell interference with sufficient accuracy (e.g., within 10-20%),sufficiently close in time (e.g., with close to TTI bandwidth) over thepower and ranges of interest. There is also a lack of other cellinterference estimation technology that accounts for a high bandwidthmeasured grant utilization probability. The consequence is that it isnot possible to make optimal scheduling decisions since the exact originof the interference power in the UL is not known with a sufficientaccuracy and bandwidth. Furthermore, it is not possible to manageinterference in heterogeneous networks (HetNets) in an optimal way. Thisfollows since different actions are needed depending on the origin ofthe interference power. This is easily understood in overloadsituations, since then the correct cell needs to receive power downcommands e.g. in the form of reduced thresholds to resolve thesituation.

Regarding HetNets in particular, problems associated with conventionalscheduling techniques can be explained in a relatively straightforwardmanner. For scheduling in the base station in general, prior techniquesrequire measurement of all UE powers in the UL. This is very costlycomputationally, requiring Kalman filters of high order for processingthe measurements to obtain estimates of the other cell interferencepower. This is because each own cell UE adds a state to the Kalmanfilter. The consequence, if such estimation cannot be done, is that thescheduler is unaware of the origin of the interference, thereby makingit more difficult to arrive at good scheduling decisions. For HetNets,the problem is again that there is no information of the origin ofinterference, and interference variance, for adjacent cells. This isprimarily due to the lack of low complexity estimators for thesequantities.

Each of one or more aspects of the disclosed subject matter addressesone or more of the issues related to conventional techniques. Forexample, recall from above that in conventional scheduling techniques,there is a delay of some number of TTIs from the scheduling decisionuntil the interference power appears over the air interface. Thescheduler also bases its scheduling decisions on a prediction of theload of the traffic it schedules. Since the UEs do not always utilizepower granted by the scheduler, the load prediction are likely to beinaccurate. The inaccuracy tends to increase as the delay increases.

A general concept applicable to one or more inventive aspects includesan algorithm to estimate other cell interferences. The algorithm may runin the RBS base band. The algorithm may be implemented any network node(e.g., RBS, RNC, CN, etc.) or any combination. An estimator implementingthe other cell interference estimation algorithm may estimate the sum ofall other cell interferences, experienced in the own cell context. Theestimator may be responsive to:

-   -   Grant utilization (which may be measured, computed)    -   Total wideband received uplink power (which may be measured)    -   Own cell load (which may be computed, scheduled), directly        related to grants.

In this discussion, the values of parameters are “estimated”,“measured”, “received” or “computed”. A measured value in essence can beviewed a number that expresses a value of a measured quantity. Anestimated value is not a number that expresses a value of a measurement,at least not directly. Rather, an estimate can be viewed as a processedset of measurements, e.g., by some filtering operation. There can alsobe received and/or computed quantities, such as time varying parametersthat are obtained from other sources. It is stressed that measured orestimated quantities can be very different, also in case the measuredand estimated quantity refer to the same underlying physical quantity,e.g., a specific power. One among many reasons for this is that theprocessing to obtain estimates e.g., may combine measurements fromdifferent times to achieve e.g., noise suppression and bias reduction.

As will be demonstrated below, one very significant advantage of theinventive estimator is its low order and associated low computationalcomplexity. In one embodiment, the estimator can be a variant of anextended Kalman filter (EKF), arranged for processing using thenonlinear interference model. One or more of the inventive aspects canbe applied to one or both of the sliding window and recursive RoTestimation algorithms. Either SIR or power based load factor calculationmay be used.

Recall from the discussion regarding HetNets that the surrounding macrocells can interfere with the low power cell to levels such that the UEsof the hotspot are actually connected to the macro cells. To addresssuch issues, in one or more aspects of disclosed subject matter, RNC(radio network controller) or the surrounding RBSs can be informed ofthe interference situation and can take action as appropriate. Forexample, admission control in the RNC or functionalities in thesurrounding RBSs can be used to reduce the other cell interference andprovide better management of the hot spot traffic, e.g., in terms of airinterface load. To enable this to take place, the RBS can includecapabilities to estimate the other cell interference.

FIG. 4 illustrates an example scenario in which a radio network node 410(e.g., eNB, eNode B, Node B, base station (BS), radio base station(RBS), and so on) can estimate the other cell interference. In thefigure, the radio network node 410 serves one or more wireless terminals430 (e.g., user equipment, mobile terminal, laptops, M2M(machine-to-machine) terminals, etc.) located within a correspondingcell 420. For clarity, the radio network node 410 will be referred to asan own radio network node, the cell 420 will be referred to as the cellof interest, and the terminals 430 within the cell of interest 420 willbe referred to as own terminals. Uplink signaling and data traffic fromthe own terminals 430 to the own radio network node 410 are illustratedas solid white arrows.

The scenario in FIG. 4 also includes other radio network nodes 415serving other wireless terminals 435 as indicated by dashed whitearrows. When the other terminals 435 transmit to their respective otherradio network nodes 415, these signals are also received in the ownradio network node 410 as indicated by shaded solid arrows. Such signalsact as interferers within the cell of interest 420. A sum of powers ofthese interfering signals experienced at the own radio network node 410at time t will be denoted as P_(other)(t). In other words, the othercell interference P_(other)(t) may be viewed as expressing a sum ofinterferences present in the cell of interest due to wireless activitiesapplicable at time t in one or more cells other than in the cell ofinterest 420. Further, there is a large solid white arrow with noparticular source. This represents the thermal noise P_(N)(t)experienced in the own radio network node 410 of the cell of interest420 at time t.

FIG. 5 illustrates an example embodiment of a radio network node 410.The radio network node 410 may comprise several devices including acontroller 510, a transceiver 520, a communicator 530 and a scheduler540. The transceiver 520 may be structured to wirelessly communicatewith wireless terminals 430. The communicator 530 may be structured tocommunicate with other network nodes and with core network nodes. Thecontroller 510 may be structured to control the overall operations ofthe radio network node 410.

FIG. 5 provides a logical view of the radio network node. It is notstrictly necessary that each device be implemented as physicallyseparate modules or circuits. Some or all devices may be combined in aphysical module. Also, one or more devices may be implemented inmultiple physical modules as illustrated in FIG. 6.

The devices of the radio network node 410 as illustrated in FIG. 5 neednot be implemented strictly in hardware. It is envisioned that any ofthe devices maybe implemented through a combination of hardware andsoftware. For example, as illustrated in FIG. 6, the radio network node410 may include one or more central processing units 610 executingprogram instructions stored in a storage 620 such as non-transitorystorage medium or firmware (e.g., ROM, RAM, Flash) to perform thefunctions of the devices. The radio network node 410 may also include atransceiver 520 structured to receive wireless signals from the wirelessterminals 430 and to send signals to the wireless terminals 430 over oneor more antennas 525 in one or more channels. The radio network node 410may further include a network interface 630 to communicate with othernetwork nodes such as the core network nodes.

In one or more aspects, the radio network node 410 can be structured toimplement a high performing estimator. The inventive estimator canperform a joint estimation of P_(other)(t)+P_(N)(t), P_(N)(t),P_(other)(t) (note the upper case “P”) and the grant utilizationprobability p_(grant)(t) (note the lower case “p”). An extended Kalmanfilter (EKF) can be used in one or more embodiments of the proposedestimator.

The proposed estimator can use any one or more of the followinginformation:

-   -   Measurements of P_(RTWP)(t), with a sampling rate of        T_(RTWP)=k_(RTWP)TTI, k_(RTWP)εZ+. Preferably, the measurements        are available for each antenna branch.    -   Computed total scheduled grants of the own cell G_(own)(t), with        a sampling rate of T_(G)=k_(G)TTI, k_(G)εZ+. Preferably,        scheduled grants are available per cell and are valid on a cell        level. They need not necessarily be valid on an antenna branch        level with Rx diversity.    -   The loop delay T_(D) between the calculation of G_(own)(t), and        the time it takes effect on the air interface. The loop delay        may be dependent on the TTI. Preferably, the loop delay is        available for and valid per cell.    -   Measured total grants of the own cell G _(own)(t), with a        sampling rate of T _(G) =k _(G) TTI, k _(G) εZ+. Preferably, the        measured total grants are available per cell, and valid on the        cell level. They need not necessarily be valid on the antenna        branch level with Rx diversity. The factors can be obtained        after TFCI decoding.

For adaptation to extended Kalman filtering, the following states aremodeled:

$\begin{matrix}{\mspace{79mu}\begin{matrix}{{x_{1,1}(t)} = {p_{grant}(t)}} & \; \\\vdots & {{{- {grant}}\mspace{14mu}{utilization}\mspace{14mu}{probabilities}},} \\{{x_{1,{{T_{D}/T} + 1}}(t)} = {p_{grant}\left( {t - T_{D}} \right)}} & \;\end{matrix}} & (12) \\\begin{matrix}{{x_{2}(t)} = {{P_{other}(t)} + {P_{N}(t)}}} & {{{- {interference}}\text{-}{and}\text{-}{noise}\mspace{14mu}{sum}\mspace{14mu}{at}\mspace{14mu}{time}\mspace{14mu} t},}\end{matrix} & (13)\end{matrix}$Modeling in one aspect may be viewed as a form of state space modelingwhere a state space of a physical system is mathematically modeled as aset of input, output and state variables related by equations.

In order to derive a measurement equation for grant utilization, the(C/I) of each user should be related to the load factor of each user.This can be done starting with (7). The idea is that the grantutilization can be modeled by a probability multiplied with the betafactor (e.g., power offset) for the EDPDCH channel for that user,assuming an on-average behavior in terms of grant utilization. Thisrenders (7) as follows:

$\begin{matrix}{{{\left( {C/I} \right)_{i,{utilized}}(t)} = {\frac{{SINR}_{i}(t)}{W_{i}}\frac{RxLoss}{G} \times \left( {1 + \frac{\begin{matrix}{{\beta_{{DPDCH},i}^{2}(t)} + {\beta_{{EDPCCH},i}^{2}(t)} +} \\{{p_{grant}(t)}\left( {{{n_{{codes},i}(t)}{\beta_{{EDPDCH},i}^{2}(t)}} + {\beta_{{HSDPCCH},i}^{2}(t)}} \right)}\end{matrix}}{\beta_{DPCCH}^{2}(t)}} \right)}},} & (14) \\{\mspace{79mu}{{i = 1},\ldots\mspace{14mu},{I.}}} & \;\end{matrix}$

Defining the known time varying quantities

$\begin{matrix}{{{k_{1,i}(t)} = {\frac{{SINR}_{i}(t)}{W_{i}}\frac{RxLoss}{G}\left( {1 + \frac{{\beta_{{DPDCH},i}^{2}(t)} + \beta_{{EDPCCH},i}^{2}}{\beta_{DPCCH}^{2}}} \right)}},{i = 1},\ldots\mspace{14mu},I} & (15) \\{{{k_{2,i}(t)} = {\frac{{SINR}_{i}(t)}{W_{i}}\frac{RxLoss}{G}\left( \frac{{n_{{codes},i}\beta_{{EDPDCH},i}^{2}} + \beta_{{HSDPCCH},i}^{2}}{\beta_{DPCCH}^{2}} \right)}},{i = 1},\ldots\mspace{14mu},I} & (16)\end{matrix}$

transforms (14) to:(C/I)_(i,utilized) =k _(1,i)(t)+k _(2,i)(t)p _(grant)(t)i=1, . . . ,I.  (17)In (17), subscript i represents the i'th user in the cell and Irepresents the total number of users.

Some algebra then transforms (6) to

$\begin{matrix}{\begin{matrix}{{L_{i}(t)} = \frac{\left( {C/I} \right)_{i}(t)}{1 + {\left( {1 - \alpha} \right)\left( {C/I} \right)_{i}(t)}}} \\{{= \frac{{k_{1,i}(t)} + {{k_{2,i}(t)}{p_{grant}(t)}}}{1 + {\left( {1 - \alpha} \right)\left( {{k_{1,i}(t)} + {{k_{2,i}(t)}{p(t)}}} \right)}}},}\end{matrix}{{i = 1},\ldots\mspace{14mu},{I.}}} & (18)\end{matrix}$

Summing up (8), then using (9) and accounting for T_(D) results in

$\begin{matrix}{{P_{RTWP}(t)} = {\frac{{P_{other}(t)} + {P_{N}(t)}}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{p_{grant}\left( {t - T_{D}} \right)}}}{\begin{matrix}{1 + \left( {1 - \alpha} \right)} \\\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{p_{grant}\left( {t - T_{D}} \right)}}} \right)\end{matrix}}}}.}} & (19)\end{matrix}$

It can be seen that the grant utilization probability p_(grant)(t) mayalso be delayed. This can indicate a need of a delay line, i.e., a stateaugmentation, in the state space filter.

Replacement with selected states and addition of a measurementdisturbance then can provide a final measurement model:

$\begin{matrix}{{y_{RTWP}(t)} = {\frac{x_{2}(t)}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}}{\begin{matrix}{1 + \left( {1 - \alpha} \right)} \\\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} - 1}}(t)}}} \right)\end{matrix}}}} + {e_{RTWP}(t)}}} & (20) \\{\mspace{79mu}{{R_{2,{RTWP}}(t)} = {{E\left\lbrack {e_{RTWP}^{2}(t)} \right\rbrack}.}}} & (21)\end{matrix}$

In (20) and (21), y_(RTWP)(t)=P_(RTWP)(t) and R_(2,RTWP)(t) denotes the(scalar) covariance matrix of e_(RTWP)(t). The known time varyingquantities k_(1,i)(t) and k_(2,i)(t) should be computed and saved tocover the entire delay T_(D).

Note that (20) represents a nonlinear load curve, expressed in terms ofthe estimated grant utilization probability x₁(t) and the estimatedinterference-and-noise sum x₂(t). That is, (20) can represent anonlinear curve expressed in terms of {circumflex over (x)}₁(t) and{circumflex over (x)}₂(t). Equation (20) can be said to relate themomentary combined effect of the estimated quantities and receivedquantities to the left hand side of the equation, i.e. the momentarymeasurement of the wideband power. Note that in one or more embodiments,the thermal noise floor N(t) can be used to represent the thermal noiseP_(N)(t) and the thermal noise floor estimate {circumflex over (N)}(t)can be used to represent thermal noise estimate {circumflex over(P)}_(N)(t) in these equations.

Measurement of the grant utilization probability p_(grant)(t) can bemade available per cell. As an example, the decoded TFCIs and E-TFCISsshow which grants the wireless terminal 430 actually used in the lastTTI. This provides the information needed to compute the sum of grantsthat are actually used, i.e. to compute:

$\begin{matrix}\begin{matrix}{{p_{grant}(t)} = \frac{{\overset{\_}{G}}_{own}\left( {t - T_{D}} \right)}{G_{own}\left( {t - T_{D}} \right)}} \\{= \frac{\begin{matrix}{{\sum\limits_{i = 1}^{n}{{{\overset{\_}{n}}_{{codes},i}\left( {t - T_{D}} \right)}{{\overset{\_}{\beta}}_{{EDPDCH},i}^{2}\left( {t - T_{T}} \right)}}} +} \\{{\overset{\_}{\beta}}_{{HSDPCCH},i}^{2}\left( {t - T_{D}} \right)}\end{matrix}}{\begin{matrix}{{\sum\limits_{i = 1}^{n}{{n_{{codes},i}\left( {t - T_{D}} \right)}\beta_{{EDPDCH},i}^{2}\left( {t - T_{T}} \right)}} +} \\{\beta_{{HSDPCCH},i}^{2}\left( {t - T_{D}} \right)}\end{matrix}}}\end{matrix} & (22)\end{matrix}$

In (22), the quantities with bars are “actual” in the sense that theyare reflected by the TFCIs, i.e., they show what the UEs are actuallyusing in their transmissions. Variables without bars denote scheduledquantities, determined by the EUL scheduler. Note that the way thegrants are measured is significant. An alternative would be to go forthe beta factors linearly. However, the relation (22) reflects the factthat the load is known to vary quadratically with the grants.

After addition of a measurement disturbance, the measurement model forthe grant utilization probability measurement becomes:y _(grantUtilization)(t)=x _(1,1)(t)+e _(grantUtilization)(t),  (23)R _(2,grantUtilization)(t)=E[e _(grantUtilization)(t)]².  (24)

Here y_(grantUtilization)(t)=p_(grant)(t) of (22). FurthermoreR_(2,grantUtilization)(t) denotes the (scalar) covariance matrix ofe_(grantUtilization)(t).

In the dynamic state model, random walk models can be adapted for thestate variables x₁(t) and x₂(t). Hence the following state model canresult from the states of (12) and (13).

$\begin{matrix}\begin{matrix}{{x\left( {t + T} \right)} \equiv \begin{pmatrix}{x_{1,1}\left( {t + T} \right)} \\\vdots \\{x_{1,{{T_{D}/T} + 1}}\left( {t + T} \right)} \\{x_{2}\left( {t + T} \right)}\end{pmatrix}} \\{= {{\begin{pmatrix}1 & 0 & \ldots & 0 & 0 \\1 & 0 & \ldots & 0 & 0 \\0 & \ddots & \ddots & \; & \ddots \\\vdots & \; & 1 & 0 & 0 \\0 & 0 & \ldots & 0 & 1\end{pmatrix}\begin{pmatrix}{x_{1,1}(t)} \\\vdots \\{x_{1,{{T_{D}/T} + 1}}(t)} \\{x_{2}(t)}\end{pmatrix}} + \begin{pmatrix}{w_{1}(t)} \\0 \\\vdots \\0 \\{w_{2}(t)}\end{pmatrix}}}\end{matrix} & (25) \\{{R_{1}(t)} = {\begin{pmatrix}{E\left\lbrack {w_{1}^{2}(t)} \right\rbrack} & 0 & \ldots & 0 & 0 \\0 & 0 & \ldots & 0 & 0 \\\vdots & \; & \ddots & \; & \vdots \\0 & \; & \; & 0 & 0 \\0 & 0 & \ldots & 0 & {E\left\lbrack {w_{2}^{2}(t)} \right\rbrack}\end{pmatrix}.}} & (26)\end{matrix}$

Preferably, the delay T equals one TTI, but can be any positive integermultiple of the TTI. In (25) and (26), quantities w₁(t), . . . , w₂(t)represent the so called system noise components, and R₁(t) representsthe associated covariance matrix.

A state space model behind the EKF can be expressed as follows:x(t+T)=A(t)x(t)+B(t)u(t)+w(t).  (27)y(t)=c(x(t))+e(t).  (28)

In (27) and (28), x(t) denotes a state vector, u(t) denotes an inputvector (not used in the inventive filtering), y(t) denotes an outputmeasurement vector comprising power measurements performed in a cell(i.e., the total received wideband power P_(RTWP)(t)), w(t) denotes theso called systems noise that represent the model error, and e(t) denotesthe measurement error. The matrix A(t) is a system matrix describing thedynamic modes, the matrix B(t) is the input gain matrix, and the vectorc(x(t)) is the, possibly nonlinear, measurement vector which is afunction of the states of the system. Finally, t represents the time andT represents the sampling period.

The general case with a nonlinear measurement vector is considered here.For this reason, the extended Kalman filter (EKF) should be applied.This filter is given by the following matrix and vector iterationsInitialization:

$\begin{matrix}{{t = t_{0}}{{\hat{x}\left( 0 \middle| {- 1} \right)} = x_{0}}{{P\left( 0 \middle| {- 1} \right)} = P_{0}}{Interation}{t = {t + T}}{{C(t)} = {\left. \frac{\partial{c(x)}}{\partial x} \middle| {}_{x = {\hat{x}{({t|{t - T}})}}}{K_{f}(t)} \right. = {{P\left( t \middle| {t - T} \right)}{C^{T}(t)}\left( {{{C(t)}{P\left( t \middle| {t - T} \right)}{C^{T}(t)}} + {R_{2}(t)}} \right)^{- 1}}}}{{\hat{x}\left( t \middle| t \right)} = {{\hat{x}\left( t \middle| {t - T} \right)} + {{K_{f}(t)}\left( {{y(t)} - {c\left( {\hat{x}\left( t \middle| {t - T} \right)} \right)}} \right)}}}{{P\left( t \middle| t \right)} = {{P\left( t \middle| {t - T} \right)} - {{K_{f}(t)}{C(t)}{P\left( t \middle| {t - T} \right)}}}}{{\hat{x}\left( {t + T} \middle| t \right)} = {{A{\hat{x}\left( t \middle| t \right)}} + {{Bu}(t)}}}{{P\left( {t + T} \middle| t \right)} = {{{{AP}\left( t \middle| t \right)}A^{T}} + {{R_{1}(t)}.{End}.}}}} & (29)\end{matrix}$

The quantities introduced in the filter iterations (29) are differenttypes of estimates ({circumflex over (x)}(t|t−T), {circumflex over(x)}(t|t), P(t|t−T), and P(t|t)), functions of such estimates (C(t) andK_(f)(t)), or other quantities (R₂(t) and R₁(t)), defined as follows:

-   -   {circumflex over (x)}(t|t−T) denotes a state prediction, based        on data up to time t−T,    -   {circumflex over (x)}(t|t) denotes a filter update, based on        data up to time t,    -   P(t|t−T) denotes a covariance matrix of the state prediction,        based on data up to time t−T,    -   P(t|t) denotes a covariance matrix of the filter update, based        on data up to time t,    -   C(t) denotes a linearized measurement matrix (linearization        around the most current state prediction),    -   K_(f)(t) denotes a time variable Kalman gain matrix,    -   R₂(t) denotes a measurement covariance matrix, and    -   R₁(t) denotes a system noise covariance matrix.

Note that R₁(t) and R₂(t) are often used as tuning variables of thefilter. In principle, the bandwidth of the filter can be controlled bythe matrix quotient of R₁(t) and R₂(t).

An example of an inventive estimation scheme using EKF will bedescribed. The quantities of the EKF for estimation of the other cellinterference and the grant utilization can now be defined. Using(19)-(20) and (22)-(25) and (28) it follows that:

$\begin{matrix}{\mspace{79mu}{{C(t)} = \begin{pmatrix}0 & \ldots & 0 & {C_{1}(t)} & {C_{2}(t)} \\1 & 0 & \ldots & \ldots & 0\end{pmatrix}}} & (30) \\{{C_{1}(t)} = \frac{{{\hat{x}}_{2}(t)}\left( t \middle| {t - T} \right){\sum\limits_{i = 1}^{I}\frac{k_{2,i}\left( {t - T_{D}} \right)}{\begin{pmatrix}{1 + \left( {1 - \alpha} \right)} \\\begin{pmatrix}{{k_{1,i}\left( {t - T_{D}} \right)} +} \\{{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}\end{pmatrix}\end{pmatrix}^{2}}}}{\left( {1 - {\sum\limits_{i = 1}^{I}\frac{\begin{matrix}{{k_{1,i}\left( {t - T_{D}} \right)} +} \\{{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}\end{matrix}}{\begin{matrix}{1 + \left( {1 - \alpha} \right)} \\\begin{pmatrix}{{k_{1,i}\left( {t - T_{D}} \right)} +} \\{{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}\end{pmatrix}\end{matrix}}}} \right)^{2}}} & (31) \\{{C_{2}(t)} = \frac{1}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}}{\begin{matrix}{1 + \left( {1 - \alpha} \right)} \\\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}} \right)\end{matrix}}}}} & (32) \\{\mspace{79mu}{{R_{2}(t)} = \begin{pmatrix}{E\left\lbrack {e_{RTWP}^{2}(t)} \right\rbrack} & 0 \\0 & {E\left\lbrack {e_{grandUtilization}^{2}(t)} \right\rbrack}\end{pmatrix}}} & (33) \\{{c\left( {\hat{x}\left( t \middle| {t - T} \right)} \right)} = \left( \frac{{\hat{x}}_{2}\left( t \middle| {t - T} \right)}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}}{\begin{matrix}{1 + \left( {1 - \alpha} \right)} \\\begin{matrix}\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)}}} \right) \\{{\hat{x}}_{1,1}\left( t \middle| {t - T_{D}} \right)}\end{matrix}\end{matrix}}}} \right)} & (34) \\{\mspace{79mu}{A = \begin{pmatrix}1 & 0 & \ldots & 0 & 0 \\1 & 0 & \ldots & 0 & 0 \\0 & \ddots & \ddots & \; & \vdots \\\vdots & \; & 1 & 0 & 0 \\0 & 0 & \ldots & 0 & 1\end{pmatrix}}} & (35) \\{\mspace{79mu}{B = 0}} & (36) \\{\mspace{79mu}{{R_{1}(t)} = {\begin{pmatrix}{E\left\lbrack {w_{1}^{2}(t)} \right\rbrack} & 0 & \ldots & 0 & 0 \\0 & 0 & \ldots & 0 & 0 \\\vdots & \; & \ddots & \; & \vdots \\0 & \; & \; & 0 & 0 \\0 & 0 & \ldots & 0 & {E\left\lbrack {w_{2}^{2}(t)} \right\rbrack}\end{pmatrix}.}}} & (37)\end{matrix}$

In order to execute the EKF, the state prediction and the statecovariance prediction at time t are needed, they are given by thefollowing equations:

$\begin{matrix}{\mspace{79mu}{{\hat{x}\left( {t - T} \right)} \equiv \begin{pmatrix}{{\hat{x}}_{1,1}\left( {t - T} \right)} \\\vdots \\{{\hat{x}}_{1,{{T_{D}/T} + 1}}\left( {t - T} \right)} \\{{\hat{x}}_{2}\left( {t - T} \right)}\end{pmatrix}}} & (38) \\{{P\left( t \middle| {t - T} \right)} = {\begin{pmatrix}{P_{1,1}\left( t \middle| {t - T} \right)} & \ldots & {P_{1,{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)} & {P_{1,2}\left( t \middle| {t\; T} \right)} \\\vdots & \ddots & \vdots & \vdots \\{P_{{{T_{D}/T} + 1},1}\left( t \middle| {t\; T} \right)} & \ldots & {P_{{{T_{D}/T} + 1},{{T_{D}/T} + 1}}\left( t \middle| {t - T} \right)} & \; \\{P_{2,1}\left( t \middle| {t - T} \right)} & \ldots & \; & {P_{2,2}\left( t \middle| {t\; T} \right)}\end{pmatrix}.}} & (39)\end{matrix}$

The equations (30)-(39) define the EKF completely, when inserted in(29). The final step to compute the other cell interference estimate canbe:{circumflex over (P)} _(other)(t|t)={circumflex over (x)}₂(t|t)−{circumflex over (P)} _(N)(t|t).  (40)

FIG. 7 illustrates a flow chart of example method 700 performed by aradio network node 410 to implement a high performing estimator. Themethod 700 may be performed by the scheduler 540, e.g., as grantutilization estimation functionality associated with the scheduler 540,to determine the other cell interference P_(other)(t). In particular,the other cell interference estimate P_(other)(t) can be determined. Theother cell interference P_(other)(t) can express a sum of interferencespresent in the cell of interest 420 due to wireless activitiesapplicable at the time t in one or more cells other than in the cell ofinterest.

As illustrated, in step 710, the radio network node 410, and inparticular the scheduler 540, can estimate the grant utilizationprobability p_(grant)(t₁) to obtain a grant utilization probabilityestimate {circumflex over (p)}_(grant)(t₁) applicable at a time t=t₁.The estimation can be made based on at least on a grant utilizationprobability estimate {circumflex over (p)}_(grant)(t) and aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀))+{circumflex over (P)}_(N)(t₀) applicable at time t=t₀.It should be noted that the term “t” enclosed in parentheses in theexpressions without subscripts (e.g., P_(other)(t), p_(load)(t), etc.)is intended to indicate time variable in general, and the same term “t”enclosed in parentheses with subscripts (e.g., P_(other)(t₀),p_(load)(t₁) etc.) is intended to indicate a particular time. Thus, timet₁ may also be viewed as t=t₁ for example.

The particular times t₀ and t₁ are assumed such that t₁−t₀=T>0. T canrepresent a duration between estimation times. In an embodiment, T is apositive integer multiple of a transmission time interval, preferablyone (e.g., for 10 ms TTI) but can be larger (e.g., 5 for 2 ms TTI). Inthe method 700, it can be assumed the values of the quantities at timet=t₀ (or simply at time t₀) are known (have been measured, computed,received, or otherwise have been determined), and the values of one ormore quantities at time t=t₁ are estimated or otherwise predicted.

In step 720, the radio network node 410 can estimate theinterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) to obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet=t₁. This estimation can be made based at least on the grantutilization probability estimate {circumflex over (p)}_(grant)(t₀) andthe interference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀)

FIG. 8 illustrates a flow chart of an example process performed by theradio network node 410 to implement the steps 710 and 720 to obtain thegrant utilization probability estimate {circumflex over (p)}_(grant)(t₁)and to obtain the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step 810, a totalscheduled grant G_(own)(t₁−T_(D)) can be calculated. Here, T_(D) canrepresent a delay between the calculation of the total scheduled grantand a time the schedule takes effect on an air interface. The totalscheduled grant G_(own)(t−T_(D)) can express an amount of the radioresources granted for use by the cell terminals 430 for uplinktransmissions at the time t.

In step 820, a total used grant G _(own)(t₁−T_(D)) can be obtained. Notethat the total used grant G _(own)(t−T_(D)) can express an amount of theradio resource grants used by the cell terminals 430 for the uplinktransmissions at the time t.

In step 830, a grant utilization

$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)}$can be measured or otherwise determined. Based on the measured grantutilization

${y_{grantUtilization} = \frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)}},$the grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) can be obtained in step 840 and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained in step850.

FIG. 9 illustrates a flow chart of another example process performed bythe radio network node 410 to implement the steps 710 and 720 to obtainthe grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) and to obtain the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step910, a total wideband power y_(RTWP)(t₁) can be measured. Based on themeasured total wideband power y_(RTWP)(t₁), the grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₁) can be obtainedin step 920, and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained instep 930.

FIG. 10 illustrates a flow chart of an example process performed by theradio network node 410 to implement the step 930 to obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step 1010, a gainfactor g(t₁) can be determined based on the grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₁) and the totalscheduled grant G_(own)(t₀). In step 1020, the measured total widebandpower y_(RTWP)(t₁) can be modeled as a combination of theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor g(t₁) and a measurement uncertainty e_(RTWP)(t₁). Based on themeasured total wideband power y_(RTWP)(t₁) and the modeling thereof, theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained.

Referring back to FIG. 7, once the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) isdetermined in step 720, the radio network node 410 can estimate theother cell interference P_(other)(t₁), i.e., obtain the other cellinterference estimate {circumflex over (P)}_(other)(t₁). The estimationcan be based at least on the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and athermal noise estimate {circumflex over (P)}_(N)(t₁). Note that theinterference-and-noise sum P_(other)(t)+P_(N)(t) can express a sum ofundesired signals, other than an own cell load P_(own)(t). In FIG. 4,the interference-and-noise sum P_(other)(t)+P_(N)(t) are visuallyillustrated with shaded arrows (from the other terminals 435) and thelarge white arrow.

It can then be seen that once the once the interference-and-noise sumestimate {circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁)is determined, the other cell interference estimate {circumflex over(P)}_(other)(t) can be arrived at if the thermal noise {circumflex over(P)}_(N)(t) can be determined. FIG. 11 illustrates a flow chart of anexample process performed by the radio network node 410 to implement thestep 730 of estimating the other cell interference P_(other)(t₁). Instep 1110, the thermal noise estimate {circumflex over (P)}_(N)(t₁) canbe obtained. In one embodiment, a thermal noise floor estimate{circumflex over (N)}(t₁) corresponding to the cell of interest 1120 canbe obtained as the thermal noise estimate {circumflex over (P)}_(N)(t₁).In step 1120, thermal noise estimate {circumflex over (P)}_(N)(t₁) canbe subtracted from the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) to obtain the othercell interference estimate {circumflex over (P)}_(other)(t₁).

FIG. 12 illustrates another flow chart of an example process performedby the radio network node 410 to implement the steps 710 and 720 toobtain the grant utilization probability estimate p_(grant)(t₁) and toobtain the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). FIG. 12 may be viewed asa specific instance of the flow chart illustrated in FIG. 8. In FIG. 12,the extended Kalman filtering adapted for estimation is used.

In step 1210, the grant utilization probabilities p_(grant)(t) and theinterference-and-noise sum P_(other)(t)+P_(N)(t) can be modeled asstates x_(1,1)(t)=p_(grant)(t)′, . . . , x_(1,T) _(D)_(/T+1)(t)=p_(grant)(t−T), x₂(t)=P_(other)(t)+P_(N)(t) in a state vectorx(t) of a state space model. The state x_(1,T) _(D) _(/T) _(TTI)₊₁(t)=p_(grant)(t−T) may reflect that the grant utilization probabilitymay be subject to a delay corresponding to the sampling period T.

In this context, the state space model can be characterized throughequations x(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t). In theseequations, x(t) represents the state vector, u(t) represents an inputvector, y(t) represents the output measurement vector, w(t) represents amodel error vector, e(t) represents a measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period. Thus, it is seen that modelingerrors and measurement errors are incorporated in the state space model.

In step 1220, the measured total wideband power y_(RTWP)(t) and themeasured grant utilization y_(grantUtilization)(t) can be modeled in theoutput measurement vector y(t) of the state space model.

In step 1230, a predicted state vector {circumflex over (x)}(t₁|t₀) canbe obtained. The predicted state vector {circumflex over (x)}(t₁|t₀)includes predicted states {circumflex over (x)}_(1,1)(t₁|t₀),{circumflex over (x)}_(1,2)(t₁|t₀) . . . {circumflex over (x)}_(1,I)(t),{circumflex over (x)}_(1,T) _(D) _(/T) _(TTI) ₊₁(t₁|t₀) and {circumflexover (x)}₂(t₁|t₀) whose values are based on the grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₀) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀). In this context, thepredicted state vector {circumflex over (x)}(t|t−T) denotes a predictionof the state vector x(t) based on information available up to time t−T.Recall from above that t₁−t₀=T>0. Thus, the predicted state vector{circumflex over (x)}(t₁|t₀) denotes a prediction the state vector x(t)at time t=t₁ based on information available up to time t=t₀. The timet=t₀ can be a time of initialization or a time of a previous iteration.

In step 1240, the predicted state vector {circumflex over (x)}(t₁|t₀)can be updated based on one or more measurements included in an outputmeasurement vector y(t₁) applicable at the time t=t₁ to obtain anestimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁). The measurements can include the measured received totalwideband power y_(RTWP)(t₁) and the grant utilizationy_(grantUtilization)(t₁). The solid white arrow entering the step 1240in FIG. 12 is to indicate that measurements may come into the step.Generally, the estimated state vector {circumflex over(x)}(t|t)={circumflex over (x)}(t) denotes an estimate of the statevector x(t) based on information available up to time t. This stepcorresponds to an adjusting step of the (extended) Kalman filteralgorithm in which the prediction made in the previous time (e.g., attime t=t₀) is adjusted according to measurements made in the currenttime (e.g., at time t=t₁).

In step 1250, the estimated states {circumflex over (x)}_(1,1)(t₁),{circumflex over (x)}_(1,2)(t₁) . . . {circumflex over (x)}_(1,I)(t₁)can be obtained from the estimated state vector {circumflex over(x)}(t₁) respectively as the grant utilization probability estimates ofthe delay line chain, {circumflex over (x)}_(1,1)(t₁)={circumflex over(p)}_(grant)(t₁), . . . , {circumflex over (x)}_(1,T) _(D)_(/T+1)(t₁)={circumflex over (p)}_(grant)(t₁−T_(D)) Also from theestimated state vector {circumflex over (x)}(t₁), estimated state{circumflex over (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflexover (P)}_(N)(t₁) may be obtained.

In step 1260, the estimated state vector {circumflex over (x)}(t₁) maybe projected based at least on dynamic modes corresponding to the cellof interest to obtain a predicted state vector {circumflex over(x)}(t₂|t₁), t₂−t₁=T. Here, the predicted state vector {circumflex over(x)}(t₂|t₁) includes predicted states {circumflex over(x)}_(1,1)(t₂|t₁), {circumflex over (x)}_(1,2)(t₂|t₁) . . . {circumflexover (x)}_(1,I)(t₁|t₁), and {circumflex over (x)}₂(t₂|t₁) whose valuesare based on the grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) and the interference-and-noise sum estimate {circumflexover (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁). This step corresponds to a predicting step of the Kalmanfilter algorithm in which future states are predicted based on currentinformation. As seen, the steps in FIG. 12 can be iteratively performed.

In one embodiment, the steps 1240 and 1260 of updating the predictedstate vector {circumflex over (x)}(t₁|t₀) and of projecting theestimated state vector {circumflex over (x)}(t₁|t₁) comprise performinga Kalman filter process to iteratively predict and update the statevector x(t) to obtain the estimated state vector {circumflex over(x)}(t). Here, the estimated state vector {circumflex over (x)}(t)includes the estimated states {circumflex over (x)}_(1,1)(t),{circumflex over (x)}_(1,2)(t) . . . {circumflex over (x)}_(1,I)(t) and{circumflex over (x)}₂(t) which may correspond to grant utilizationprobability estimates {circumflex over (p)}_(grant)(t₁) of the delayline chain and the interference-and-noise sum estimate {circumflex over(x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁).

FIG. 13 illustrates a flow chart of an example process performed by theradio network node 410 to implement the step 1240 to update predictedstate vector {circumflex over (x)}(t₁|t₀). Here, it is assumed that thestate x_(1,T) _(D) _(/T) _(TTI) ₊₁(t) is also modeled in the statevector {circumflex over (x)}(t). In step 1310, the measured totalwideband power y_(RTWP)(t₁) applicable at the time t=t₁ can be modeledas:

$\begin{matrix}{{y_{RTWP}(t)} = {\frac{x_{2}(t)}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}}{1 + {\left( {1 - \alpha} \right)\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}} \right)}}}} + {{e_{RTWP}(t)}.}}} & (41)\end{matrix}$Here, T_(D) can represent a delay between calculation of the scheduleand a time the schedule takes effect on an air interface. Also,e_(RTWP)(t) can represent a measurement error.

In step 1320, the grant utilization y_(grantUtilization)(t₁) applicableat the time t=t₁ as can be modeled as:y _(grantUtilization)(t)=x _(1,1)(t)+e _(grantUtilization)(t).  (42)Again, e_(grantUtilization)(t) can represent a measurement error.

In step 1330, a measurement matrix C(t₁) around the predicted statevector {circumflex over (x)}(t₁|t₀) can be obtained. Here, the predictedstate vector {circumflex over (x)}(t₁|t₀) can include the predictedstates {circumflex over (x)}_(1,1)(t₁|t₀), {circumflex over(x)}_(1,2)(t₁|t₀) . . . {circumflex over (x)}_(1,I)(t₁|t₀), {circumflexover (x)}_(1,T) _(D) _(/T) _(TTI) ₊₁(t₁|t₀) and {circumflex over(x)}₂(t₁|t₀) which are predicted based on data up to the time t=t₀. Inan embodiment, the measurement matrix C(t₁) can be obtained bydetermining the measurement matrix C(t₁) linearized around the predictedstate vector {circumflex over (x)}(t₁|t₀) such that

${C(t)} = \left. \frac{\partial{c(x)}}{\partial x} \middle| {}_{x = {\hat{x}{({{t\; 1}|{t\; 0}})}}}. \right.$

In step 1340, a Kalman gain matrix K_(f)(t₁) can be obtained based on atleast the measurement matrix C(t₁), the measurement error vector e(t₁),and a predicted covariance matrix P(t₁|t₀) corresponding to thepredicted state vector {circumflex over (x)}(t₁|t₀). In an embodiment,the Kalman gain matrix K_(f)(t₁) can be obtained by determining:K _(f)(t ₁)=P(t ₁ |t ₀)C ^(T)(t ₁)(C(t ₁)P(t ₁ |t ₀)C ^(T)(t ₁)+R ₂(t₁))⁻¹  (43)in which C^(T)(t) is a transpose of the measurement matrix C(t) and(R₂(t)) is a measurement covariance matrix corresponding to themeasurement error vector e(t).

In step 1350, the predicted state vector {circumflex over (x)}(t₁|t₀)can be updated based on at least the Kalman gain matrix K_(f)(t₁), theoutput measurement vector y(t₁), and the measurement vector c(x(t₁)) toobtain the estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁). The estimated state vector{circumflex over (x)}(t₁) can include the estimated states {circumflexover (x)}_(1,1)(t₁), {circumflex over (x)}_(1,2)(t₁) . . . {circumflexover (x)}_(1,I)(t₁), {circumflex over (x)}_(1,T) _(D) _(/T) _(TTI)₊₁(t₁) and {circumflex over (x)}₂(t₁). In an embodiment, the estimatedstate vector {circumflex over (x)}(t₁|t₁)={circumflex over (x)}(t₁) canbe obtained through determining:{circumflex over (x)}(t ₁ |t ₁)={circumflex over (x)}(t ₁ |t ₀)+K _(f)(t₁)(y(t ₁)−c({circumflex over (x)}(t ₁ |t ₀))).  (45)Here y(t₁) is the measurement vector, with components being the receivedtotal wideband power measurement and the grant utilization measurement.

In step 1360, the predicted covariance matrix P(t₁|t₀) can be updatedbased on at least the Kalman gain matrix K_(f)(t₁) and the measurementmatrix C(t₁) to obtain an updated covariance matrix P(t₁|t₁)corresponding to the estimated state vector {circumflex over (x)}(t₁).In an embodiment, the updated covariance matrix P(t₁|t₁) can be obtainedthrough determining:P(t ₁ |t ₁)=P(t ₁ |t ₀)−K _(f)(t ₁)C(t ₁)P(t ₁ |t ₀).  (46)

Referring back to FIG. 12, the step 1260 of projecting the estimatedstate vector {circumflex over (x)}(t₁) can comprise projecting theestimated state vector {circumflex over (x)}(t₁) based on at least thesystem matrix A(t₁) to obtain the predicted state vector {circumflexover (x)}(t₂|t₁). Here, the predicted state vector {circumflex over(x)}(t₂|t₁) includes the predicted states {circumflex over(x)}_(1,1)(t₂|t₁), {circumflex over (x)}_(1,2)(t₂|t₁) . . . {circumflexover (x)}_(1,I)(t₂|t₁), {circumflex over (x)}_(1,T) _(D) _(/T) _(TTI)₊₁(t₂|t₁) and {circumflex over (x)}₁(t₂|t₁). Then in step 1270, theupdated covariance matrix P(t₁|t₁) can be projected to obtain apredicted covariance matrix P(t₂|t₁) based on at least the system matrixA(t₁) and a system noise covariance matrix R₁(t₁). Back in step 1260,the predicted state vector {circumflex over (x)}(t₂|t₁) can be obtainedby determining {circumflex over (x)}(t₂|t₁)=A{circumflex over(x)}(t₁|t₁)+Bu(t₁), and in step 1270, the predicted covariance matrixP(t₂|t₁) can be obtained through determiningP(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transpose of thesystem matrix A(t). Note that the input gain matrix B(t) can be set tozero.

There are several advantages of the disclosed subject matter. Among themis in enhancing the performance of the whole mobile broadband cellularsystem, e.g., WCDMA RAN. This can lead to (among others):

Enhanced interference management in HetNets;

Corresponding enhanced system capacity.

Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the disclosed subject matterbut as merely providing illustrations of some of the presently preferredembodiments. Therefore, it will be appreciated that the scope of thedisclosed subject matter fully encompasses other embodiments, and thatthe scope is accordingly not to be limited. All structural, andfunctional equivalents to the elements of the above-described preferredembodiment that are known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed hereby. Moreover, it is not necessary for a device or methodto address each and every problem described herein or sought to besolved by the present technology, for it to be encompassed hereby.

What is claimed is:
 1. A method (700) performed at a radio network node(410) corresponding to a cell of interest (420) in a wireless network(400), the radio network node comprising a processor configured toexecute instructions store in memory, the method (700) comprising:estimating (710), by the processor, a grant utilization probabilityp_(grant)(t₁) based at least on a grant utilization probability estimate{circumflex over (p)}_(grant)(t₀) and an interference-and-noise sumestimate {circumflex over (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀)applicable at a time t₀ to obtain a grant utilization probabilityestimate {circumflex over (p)}_(grant)(t₁) applicable at a time t₁,wherein t₁−t₀=T>0; estimating (720), by the processor, aninterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) based at least on thegrant utilization probability estimate {circumflex over (p)}_(grant)(t₁)and the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁; and estimating (730), by the processor, an other cell interferenceP_(other)(t₁) based at least on the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and athermal noise estimate {circumflex over (P)}_(N)(t₁) to obtain an othercell interference estimate {circumflex over (P)}_(other)(t₁) applicableat the time t₁, wherein the grant utilization probability p_(grant)(t)expresses a relationships between radio resource grants scheduled to oneor more cell terminals and radio resource grants used by the same cellterminals applicable at a time t, each cell terminal being a wirelessterminal in the cell of interest, and the grant utilization probabilityestimate {circumflex over (p)}_(grant)(t) being an estimate thereof,wherein the interference-and-noise sum P_(other)(t)+P_(N)(t) expresses asum of undesired signals, other than an own cell load P_(own)(t),applicable at the time t, and the interference-and-noise sum estimate{circumflex over (P)}_(other)(t)+{circumflex over (P)}_(N)(t) being anestimate thereof, wherein the own cell load P_(own)(t) expresses a sumof signals due to wireless activities in the cell of interest applicableat the time t, wherein the other cell interference P_(other)(t)expresses a sum of interferences present in the cell of interest due towireless activities applicable at the time t in one or more cells otherthan in the cell of interest, and the other cell interference estimate{circumflex over (P)}_(other)(t) being an estimate thereof, and whereina thermal noise P_(N)(t) expresses a sum of undesired signals present inthe cell of interest at the time t other than the own cell loadP_(own)(t) and other than the other cell interference P_(other)(t), andthe thermal noise estimate {circumflex over (P)}_(N)(t) being anestimate thereof.
 2. The method of claim 1, wherein estimating, by theprocessor, the grant utilization probability p_(load)(t₁) and estimatingthe interference-and-noise sum P_(other)(t₁)+P_(N)(t₁) comprise:calculating a total scheduled grant G_(own)(t₁−T_(D)); obtaining a totalused grant G _(own)(t₁−T_(D)); measuring a grant utilization$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)};$obtaining the grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) based on the measured grant utilization$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)};$and obtaining the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredgrant utilization$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)},$wherein the total scheduled grant G_(own)(t−T_(D)) expresses an amountof the radio resources granted for use by the cell terminals for uplinktransmissions at the time t, and wherein the total used grant G_(own)(t₁−T_(D)) expresses an amount of the radio resource grants usedby the cell terminals for the uplink transmissions at the time t, andwherein T_(D) represents a delay between calculation of the totalscheduled grant and a time the schedule takes effect on an airinterface.
 3. The method of claim 1, wherein estimating, by theprocessor, the grant utilization probability p_(grant)(t₁) andestimating the interference-and-noise sum P_(other)(t₁)+P_(N)(t₁)comprise: measuring a total wideband power y_(RTWP)(t₁): obtaining thegrant utilization probability estimate {circumflex over (p)}_(grant)(t₁)based on the measured total wideband power y_(RTWP)(t₁); and obtainingthe interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁).
 4. The method of claim 3, whereinobtaining the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) comprises: determining again factor g(t₁) based on the grant utilization probability estimate{circumflex over (p)}_(load)(t₁) and the total scheduled grantG_(own)(t₀); modeling the measured total wideband power y_(RTWP)(t₁) asa combination of the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor g(t₁) and a measurement uncertainty e_(RTWP)(t₁); and obtainingthe interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁) and the model thereof.
 5. The methodof claim 1, wherein estimating, by the processor, the grant utilizationprobability p_(grant)(t₁) and estimating the interference-and-noise sumP_(other)(t₁)+P_(N)(t₁) comprise: modeling the grant utilizationprobability p_(grant)(t) and the interference-and-noise sumP_(other)(t)+P_(N)(t) as states x_(1, 1)(t) = p_(grant)(t) ⋮x_(1, T_(D)/T + 1)(t) = p_(grant)(t − T) and x₂(t)=P_(other)(t)+P_(N)(t)in a state vector x(t) of a state space model; modeling a measured totalwideband power y_(RTWP)(t) and a measured grant utilizationy_(grantUtilization)(t) in an output measurement vector y(t) of thestate space model; obtaining a predicted state vector {circumflex over(x)}(t₁|t₀) which includes therein predicted states {circumflex over(x)}_(1,1)(t₁|t₀), {circumflex over (x)}_(1,2)(t₁|t₀) . . . {circumflexover (x)}_(1,I)(t₁|t₀), {circumflex over (x)}_(1,T) _(D) _(/T) _(TTI)₊₁(t₁|t₀), {circumflex over (x)}₂(t₁|t₀) whose values are based on thegrant utilization probability estimate {circumflex over (p)}_(grant)(t₀)and the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀); updating the predictedstate vector {circumflex over (x)}(t₁|t₀) based on one or moremeasurements included in an output measurement vector y(t₁) applicableat the time t₁ to obtain an estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁); and obtaining estimated states{circumflex over (x)}_(1,1)(t₁), {circumflex over (x)}_(1,2)(t₁) . . .{circumflex over (x)}_(1,I)(t₁), {circumflex over (x)}₂(t₁) from theestimated state vector {circumflex over (x)}(t₁) as the grantutilization probability estimate of the delay line chain {circumflexover (x)}_(1,1)(t₁)={circumflex over (p)}_(grant)(t₁), . . . ,{circumflex over (x)}_(1,T) _(D) _(/T+1)(t₁)={circumflex over(p)}_(grant)(t₁−t_(D)), and the interference-and-noise sum estimate{circumflex over (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflexover (P)}_(N)(t₁), wherein modeling errors and measurement errors areincorporated in the state space model as a model error vector w(t) and ameasurement error vector e(t), wherein the predicted state vector{circumflex over (x)}(t|t−T) denotes a prediction of the state vectorx(t) based on information available up to a time t−T, and wherein theestimated state vector {circumflex over (x)}(t|t)={circumflex over(x)}(t) denotes an estimate of the state vector x(t) based oninformation available up to the time t.
 6. The method of claim 5,wherein updating the predicted state vector {circumflex over (x)}(t₁|t₀)comprises: modeling the measured total wideband power y_(RTWP)(t₁)applicable at the time t₁ as${{y_{RTWP}(t)} = {\frac{x_{2}(t)}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}}{1 + {\left( {1 - \alpha} \right)\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}} \right)}}}} + {e_{RTWP}(t)}}},$T_(D) representing a delay between calculation of the schedule and atime the schedule takes effect on an air interface; modeling the grantutilization y_(grantUtilization)(t₁) applicable at the time t₁ asy_(grantUtilization)(t₁)=x_(1,1)(t₁)+e_(grantUtilization)(t₁); obtaininga measurement matrix C(t₁) around the predicted state vector {circumflexover (x)}(t₁|t₀), the predicted state vector {circumflex over(x)}(t₁|t₀) including the predicted states predicted states {circumflexover (x)}_(1,1)(t₁|t₀), {circumflex over (x)}_(1,2)(t₁|t₀) . . .{circumflex over (x)}_(1,I)(t₁|t₀), {circumflex over (x)}_(1,T) _(D)_(/T) _(TTI) ₊₁(t₁|t₀) and {circumflex over (x)}₂(t₁|t₀) predicted basedon data up to the time t₀; obtaining a Kalman gain matrix K_(f)(t₁)based on at least the measurement matrix C(t₁), the measurement errorvector e(t₁), and a predicted covariance matrix P(t₁|t₀) correspondingto the predicted state vector {circumflex over (x)}(t₁|t₀); updating thepredicted state vector {circumflex over (x)}(t₁|t₀) based on at leastthe Kalman gain matrix K_(f)(t₁), the output measurement vector y(t₁),and the measurement vector c(x(t₁)) to obtain the estimated state vector{circumflex over (x)}(t₁|t₁)={circumflex over (x)}(t₁), the estimatedstate vector {circumflex over (x)}(t₁) including the estimated states{circumflex over (x)}_(1,1)(t₁), {circumflex over (x)}_(1,2)(t₁) . . .{circumflex over (x)}_(1,I)(t₁), {circumflex over (x)}_(1,T) _(D) _(/T)_(TTI) ₊₁(t₁) and {circumflex over (x)}₂(t₁); and updating the predictedcovariance matrix P(t₁|t₀) based on at least the Kalman gain matrixK_(f)(t₁) and the measurement matrix C(t₁) to obtain an updatedcovariance matrix P(t₁|t₁) corresponding to the estimated state vector{circumflex over (x)}(t₁).
 7. The method of claim 6, wherein obtainingthe measurement matrix C(t₁) comprises determining the measurementmatrix C(t₁) linearized around the predicted state vector {circumflexover (x)}(t₁|t₀) such that${{C(t)} = \left. \frac{\partial{c(x)}}{\partial x} \right|_{x = {\hat{x}{({{t\; 1}|{t\; 0}})}}}},$wherein obtaining the Kalman gain matrix K_(f)(t₁) comprises determiningK_(f)(t₁)=P(t₁|t₀)C^(T)(t₁)(C(t₁)P(t₁|t₀)C^(T)(t₁)+R₂(t₁))⁻¹ in whichC^(T)(t) is a transpose of the measurement matrix C(t) and (R₂(t)) is ameasurement covariance matrix corresponding to the measurement errorvector e(t), wherein updating the predicted state vector {circumflexover (x)}(t₁|t₀) to obtain the estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁) comprises determining {circumflexover (x)}(t₁|t₁)={circumflex over(x)}(t₁|t₀)+K_(f)(t₁)(y(t₁)−c({circumflex over (x)}(t₁|t₀))), andwherein updating the predicted covariance matrix P(t₁|t₀) to obtain theupdated covariance matrix P(t₁|t₁) comprises determiningP(t₁|t₁)=P(t₁|t₀)−K_(f)(t₁)C(t₁)P(t₁|t₀).
 8. The method of claim 5,further comprising: projecting, by the processor, the estimated statevector {circumflex over (x)}(t₁) based at least on dynamic modescorresponding to the cell of interest to obtain a predicted state vector{circumflex over (x)}(t₂|t₁), t₂−t₁=T, wherein the predicted statevector {circumflex over (x)}(t₂|t₁) includes predicted states{circumflex over (x)}_(1,1)(t₂|t₁), {circumflex over (x)}_(1,2)(t₂|t₁) .. . {circumflex over (x)}_(1,I)(t₂|t₁), {circumflex over (x)}_(1,T) _(D)_(/T) _(TTI) ₊₁(t₂|t₁) and {circumflex over (x)}₂(t₂|t₁), and whereinthe state space model is characterized through equationsx(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t), in which x(t)represents the state vector, u(t) represents an input vector, y(t)represents the output measurement vector, w(t) represents the modelerror vector, e(t) represents the measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period.
 9. The method of claim 6, whereinthe method further comprises projecting, by the processor, the updatedcovariance matrix P(t₁|t₁) to obtain a predicted covariance matrixP(t₂|t₁) based on at least the system matrix A(t₁) and a system noisecovariance matrix R₁(t₁).
 10. The method of claim 9, wherein projectingthe estimated state vector {circumflex over (x)}(t₁) to obtain thepredicted state vector {circumflex over (x)}(t₂|t₁) comprisesdetermining {circumflex over (x)}(t₂|t₁)=A{circumflex over(x)}(t₁|t₁)+Bu(t₁), and wherein projecting the updated covariance matrixP(t₁|t₁) to obtain the predicted covariance matrix P(t₂|t₁) comprisesdetermining P(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transposeof the system matrix A(t).
 11. The method of claim 1, wherein estimatingthe other cell interference P_(other)(t₁) comprises: obtaining a thermalnoise floor estimate {circumflex over (N)}(t₁) corresponding to the cellof interest as the thermal noise estimate {circumflex over (P)}_(N)(t₁);and subtracting the thermal noise estimate {circumflex over (P)}_(N)(t₁)from the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) to obtain the other cellinterference estimate {circumflex over (P)}_(other)(t₁).
 12. The methodof claim 1, wherein T is equal to a transmission time interval (TTI).13. A radio network node of a wireless network, the radio network nodecorresponding to a cell of interest and being structured to determine another cell interference P_(other)(t) the radio network node comprising:a transceiver structured to transmit and receive wireless signals viaone or more antennas from and to one or more cell terminals locatedwithin the cell of interest; a communicator structured to communicatewith other network nodes; and a scheduler structured to schedule uplinktransmissions from the cell terminals, wherein the scheduler isstructured to: estimate a grant utilization probability p_(grant)(t₁)based at least on a grant utilization probability estimate {circumflexover (p)}_(grant)(t₀) and an interference-and-noise sum estimate{circumflex over (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀)applicable at a time t₀ to obtain a grant utilization probabilityestimate {circumflex over (p)}_(grant)(t₁) applicable at a time t₁,wherein t₁−t₀=T>0, estimate an interference-and-noise sumP_(other)(t₁)+P_(N)(t₁) based at least on the grant utilizationprobability estimate {circumflex over (p)}_(grant)(t₁) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁, and estimate an other cell interference P_(other)(t₁) based at leaston the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and a thermal noiseestimate {circumflex over (P)}_(N)(t₁) to obtain an other cellinterference estimate {circumflex over (P)}_(other)(t₁) applicable atthe time t₁, wherein the grant utilization probability p_(grant)(t)expresses a relationships between radio resource grants scheduled to oneor more cell terminals and radio resource grants used by the same cellterminals applicable at a time t, each cell terminal being a wirelessterminal in the cell of interest, and the grant utilization probabilityestimate {circumflex over (p)}_(grant)(t) being an estimate thereof,wherein the interference-and-noise sum P_(other)(t)+P_(N)(t) expresses asum of undesired signals, other than an own cell load P_(own)(t),applicable at the time t, and the interference-and-noise sum estimate{circumflex over (P)}_(other)(t)+{circumflex over (P)}_(N)(t) being anestimate thereof, wherein the own cell load P_(own)(t) expresses a sumof signals due to wireless activities in the cell of interest applicableat the time t, wherein the other cell interference P_(other)(t)expresses a sum of interferences present in the cell of interest due towireless activities applicable at the time t in one or more cells otherthan in the cell of interest, and the other cell interference estimate{circumflex over (P)}_(other)(t) being an estimate thereof, and whereina thermal noise P_(N)(t) expresses a sum of undesired signals present inthe cell of interest at the time t other than the own cell loadP_(own)(t) and other than the other cell interference P_(other)(t) andthe thermal noise estimate {circumflex over (P)}_(N)(t) being anestimate thereof.
 14. The radio network node of claim 13, wherein inorder to estimate the grant utilization probability p_(load)(t₁) and toestimate the interference-and-noise sum P_(other)(t₁)+{circumflex over(P)}_(N)(t₁), the scheduler is structured to: calculate a totalscheduled grant G_(own)(t₁−T_(D)), obtain a total used grant G_(own)(t₁−T_(D)), measure a grant utilization$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)},$and obtain the grant utilization probability estimate {circumflex over(p)}_(grant)(t₁) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on themeasured grant utilization$\frac{{\overset{\_}{G}}_{own}\left( {t_{1} - T_{D}} \right)}{G_{own}\left( {t_{1} - T_{D}} \right)},$wherein the total scheduled grant G_(own)(t−T_(D)) expresses an amountof the radio resources granted for use by the cell terminals for uplinktransmissions at the time t, and wherein the total used grant G_(own)(t₁−T_(D)) expresses an amount of the radio resource grants usedby the cell terminals for the uplink transmissions at the time t, andwherein T_(D) represents a delay between calculation of the totalscheduled grant and a time the schedule takes effect on an airinterface.
 15. The radio network node of claim 13, wherein in order toestimate the grant utilization probability p_(load)(t₁) and to estimatethe interference-and-noise sum P_(other)(t₁)+P_(N)(t₁), the scheduler isstructured to: measure a total wideband power y_(RTWP)(t₁), obtain thegrant utilization probability estimate {circumflex over (p)}_(load)(t₁)based on the measured total wideband power y_(RTWP)(t₁), and obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁).
 16. The radio network node of claim15, wherein in order to obtain the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁), thescheduler is structured to: determine a gain factor g(t₁) based on thegrant utilization probability estimate {circumflex over (p)}_(grant)(t₁)and the total scheduled grant G_(own)(t₀), model the measured totalwideband power y_(RTWP)(t₁) as a combination of theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor g(t₁) and a measurement uncertainty e_(RTWP)(t₁); and obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁) and the model thereof.
 17. The radionetwork node of claim 13, wherein in order to estimate the grantutilization probability p_(load)(t₁) and to estimate theinterference-and-noise sum P_(other)(t₁)+P_(N)(t₁), the scheduler isstructured to: model the grant utilization probability p_(grant)(t) andthe interference-and-noise sum P_(other)(t)+P_(N)(t) as statesx_(1, 1)(t) = p_(grant)(t) ⋮ x_(1, T_(D)/T + 1)(t) = p_(grant)(t − T)and x₂(t)=P_(other)(t)+P_(N)(t) in a state vector x(t) of a state spacemodel, model a measured total wideband power y_(RTWP)(t) and a measuredgrant utilization y_(grantUtilization)(t) in an output measurementvector y(t) of the state space model, obtain a predicted state vector{circumflex over (x)}(t₁|t₀) which includes therein predicted states{circumflex over (x)}_(1,1)(t₁|t₀), {circumflex over (x)}_(1,2)(t₁|t₀) .. . {circumflex over (x)}_(1,I)(t₁|t₀), {circumflex over (x)}_(1,T) _(D)_(/T) _(TTI) ₊₁(t₁|t₀), {circumflex over (x)}₂(t₁|t₀) whose values arebased on the grant utilization probability estimate {circumflex over(p)}_(grant)(t₀) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀), update thepredicted state vector {circumflex over (x)}(t₁|t₀) based on one or moremeasurements included in an output measurement vector y(t₁) applicableat the time t₁ to obtain an estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁), and obtain estimated states{circumflex over (x)}_(1,1)(t₁), {circumflex over (x)}_(1,2)(t₁) . . .{circumflex over (x)}_(1,I)(t₁), {circumflex over (x)}₂(t₁) from theestimated state vector {circumflex over (x)}(t₁) as the grantutilization probability estimate of the delay line chain, {circumflexover (x)}_(1,1)(t₁)={circumflex over (p)}_(grant)(t₁), . . . ,{circumflex over (x)}_(1,T) _(D) _(/T+1)(t₁)={circumflex over(p)}_(grant)(t₁−T_(D)) and the interference-and-noise sum estimate{circumflex over (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflexover (P)}_(N)(t₁), wherein modeling errors and measurement errors areincorporated in the state space model as a model error vector w(t) and ameasurement error vector e(t), wherein the predicted state vector{circumflex over (x)}(t|t−T) denotes a prediction of the state vectorx(t) based on information available up to a time t−T, and wherein theestimated state vector {circumflex over (x)}(t|t)={circumflex over(x)}(t) denotes an estimate of the state vector x(t) based oninformation available up to the time t.
 18. The radio network node ofclaim 17, wherein in order to update the predicted state vector{circumflex over (x)}(t₁|t₀), the scheduler is structured to: model themeasured total wideband power y_(RTWP)(t₁) applicable at the time t₁ as${{y_{RTWP}(t)} = {\frac{x_{2}(t)}{1 - {\sum\limits_{i = 1}^{I}\frac{{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}}{1 + {\left( {1 - \alpha} \right)\left( {{k_{1,i}\left( {t - T_{D}} \right)} + {{k_{2,i}\left( {t - T_{D}} \right)}{x_{1,{{T_{D}/T} + 1}}(t)}}} \right)}}}} + {e_{RTWP}(t)}}},$T_(D) representing a delay between calculation of the schedule and atime the schedule takes effect on an air interface, model the grantutilization y_(grantUtilization)(t₁) applicable at the time t₁ asy_(grantUtilization)(t₁)=x_(1,1)(t₁)+e_(grantUtilization)(t₁), obtain ameasurement matrix C(t₁) around the predicted state vector {circumflexover (x)}(t₁|t₀), the predicted state vector {circumflex over(x)}(t₁|t₀) including the predicted states {circumflex over(x)}_(1,1)(t₁|t₀), {circumflex over (x)}_(1,2)(t₁|t₀) . . . {circumflexover (x)}_(1,I)(t₁|t₀), {circumflex over (x)}_(1,T) _(D) _(/T) _(TTI)₊₁(t₁|t₀) and {circumflex over (x)}₂(t₁|t₀) predicted based on data upto the time t₀, obtain a Kalman gain matrix K_(f)(t₁) based on at leastthe measurement matrix C(t₁), the measurement error vector e(t₁), and apredicted covariance matrix P(t₁|t₀) corresponding to the predictedstate vector {circumflex over (x)}(t₁|t₀), update the predicted statevector {circumflex over (x)}(t₁|t₀) based on at least the Kalman gainmatrix K_(f)(t₁), the output measurement vector y(t₁), and themeasurement vector c(x(t₁)) to obtain the estimated state vector{circumflex over (x)}(t₁|t₁)={circumflex over (x)}(t₁), the estimatedstate vector {circumflex over (x)}(t₁) including the estimated states{circumflex over (x)}_(1,1)(t₁), {circumflex over (x)}_(1,2)(t₁) . . .{circumflex over (x)}_(1,I)(t₁), {circumflex over (x)}_(1,T) _(D) _(/T)_(TTI) ₊₁(t₁) and {circumflex over (x)}₂(t₁) and update the predictedcovariance matrix P(t₁|t₀) based on at least the Kalman gain matrixK_(f)(t₁) and the measurement matrix C(t₁) to obtain an updatedcovariance matrix P(t₁|t₁) corresponding to the estimated state vector{circumflex over (x)}(t₁).
 19. The radio network node of claim 18,wherein the scheduler is structured to: determine the measurement matrixC(t₁) linearized around the predicted state vector {circumflex over(x)}(t₁|t₀) such that${C(t)} = \left. \frac{\partial{c(x)}}{\partial x} \right|_{x = {\hat{x}{({{t\; 1}|{t\; 0}})}}}$to obtain the measurement matrix C(t₁), determineK_(f)(t₁)=P(t₁|t₀)C^(T)(t₁)(C(t₁)P(t₁|t₀)C^(T)(t₁)+R₂(t₁))⁻¹ in whichC^(T)(t) is a transpose of the measurement matrix C(t) and (R₂(t)) is ameasurement covariance matrix corresponding to the measurement errorvector e(t) to obtain the Kalman gain matrix K_(f)(t₁), determine{circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁|t₀)+K_(f)(t₁)(y(t₁)−c({circumflex over (x)}(t₁|t₀))) to updatethe predicted state vector {circumflex over (x)}(t₁|t₀) to obtain theestimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁), and determine P(t₁|t₁)=P(t₁|t₀)−K_(f)(t₁)C(t₁)P(t₁|t₀) toupdate the predicted covariance matrix P(t₁|t₀) to obtain the updatedcovariance matrix P(t₁|t₁).
 20. The radio network node of claim 18,wherein the scheduler is structured to project the estimated statevector {circumflex over (x)}(t₁) based at least on dynamic modescorresponding to the cell of interest to obtain a predicted state vector{circumflex over (x)}(t₂|t₁), t₂−t₁=T, wherein the predicted statevector {circumflex over (x)}(t₂|t₁) includes predicted states{circumflex over (x)}_(1,1)(t₂|t₁), {circumflex over (x)}_(1,2)(t₂|t₁) .. . {circumflex over (x)}_(1,I)(t₂|t₁), {circumflex over (x)}_(1,T) _(D)_(/T) _(TTI) ₊₁(t₂|t₁) and {circumflex over (x)}₂(t₂|t₁), and whereinthe state space model is characterized through equationsx(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t), in which x(t)represents the state vector, u(t) represents an input vector, y(t)represents the output measurement vector, w(t) represents the modelerror vector, e(t) represents the measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period.
 21. The radio network node of claim18, wherein the scheduler is structured to project the updatedcovariance matrix P(t₁|t₁) to obtain a predicted covariance matrixP(t₂|t₁) based on at least the system matrix A(t₁) and a system noisecovariance matrix R₁(t₁).
 22. The radio network node of claim 21,wherein the scheduler is structured to: determine {circumflex over(x)}(t₂|t₁)=A{circumflex over (x)}(t₁|t₁)+Bu(t₁) to project theestimated state vector {circumflex over (x)}(t₁) to obtain the predictedstate vector {circumflex over (x)}(t₂|t₁), and determineP(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transpose of thesystem matrix A(t) to project the updated covariance matrix P(t₁|t₁) toobtain the predicted covariance matrix P(t₂|t₁).
 23. The radio networknode of claim 13, wherein in order to estimate the other cellinterference P_(other)(t₁), the scheduler is structured to: obtain athermal noise floor estimate {circumflex over (N)}(t₁) corresponding tothe cell of interest as the thermal noise estimate {circumflex over(P)}_(N)(t₁), and subtract the thermal noise estimate {circumflex over(P)}_(N)(t₁) from the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) to obtain the othercell interference estimate {circumflex over (P)}_(other)(t₁).
 24. Theradio network node of claim 13, wherein T is equal to a transmissiontime interval (TTI).
 25. A non-transitory computer-readable medium whichhas stored therein programming instructions, wherein when a computerexecutes the programming instructions, the computer executes a methodperformed in a radio network node of a wireless network for determiningother cell interference applicable, wherein the method is the method ofclaim 1.